Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions
R Sharma, RB Pachori - Expert Systems with Applications, 2015 - Elsevier
Epileptic seizure is the most common disorder of human brain, which is generally detected
from electroencephalogram (EEG) signals. In this paper, we have proposed the new …
from electroencephalogram (EEG) signals. In this paper, we have proposed the new …
Hilbert marginal spectrum analysis for automatic seizure detection in EEG signals
K Fu, J Qu, Y Chai, T Zou - Biomedical Signal Processing and Control, 2015 - Elsevier
In this paper, we present a new technique for automatic seizure detection in
electroencephalogram (EEG) signals by using Hilbert marginal spectrum (HMS) analysis. As …
electroencephalogram (EEG) signals by using Hilbert marginal spectrum (HMS) analysis. As …
Analysis of high-dimensional phase space via Poincaré section for patient-specific seizure detection
In this paper, the performance of the phase space representation in interpreting the
underlying dynamics of epileptic seizures is investigated and a novel patient-specific seizure …
underlying dynamics of epileptic seizures is investigated and a novel patient-specific seizure …
Designing a robust feature extraction method based on optimum allocation and principal component analysis for epileptic EEG signal classification
The aim of this study is to design a robust feature extraction method for the classification of
multiclass EEG signals to determine valuable features from original epileptic EEG data and …
multiclass EEG signals to determine valuable features from original epileptic EEG data and …
Epileptic seizure prediction by exploiting spatiotemporal relationship of EEG signals using phase correlation
Automated seizure prediction has a potential in epilepsy monitoring, diagnosis, and
rehabilitation. Electroencephalogram (EEG) is widely used for seizure detection and …
rehabilitation. Electroencephalogram (EEG) is widely used for seizure detection and …
Seizure detection using regression tree based feature selection and polynomial SVM classification
Z Zhang, KK Parhi - … 37th annual international conference of the …, 2015 - ieeexplore.ieee.org
This paper presents a novel patient-specific algorithm for detection of seizures in epileptic
patients with low hardware complexity and low power consumption. In the proposed …
patients with low hardware complexity and low power consumption. In the proposed …
Automatic detection of epileptic seizures in long-term EEG records
Epilepsy is a neurological disorder which affects nearly 1.5% of the world׳ s total population.
Trained physicians and neurologists visually scan the long-term electroencephalographic …
Trained physicians and neurologists visually scan the long-term electroencephalographic …
Empirical mode decomposition-based detection of bend-induced error and its correction in a Raman optical fiber distributed temperature sensor
The calibration of Raman scattering-based optical fiber distributed temperature sensor
(OFDTS) is performed using temperature of the integrated reference (calibration) loop …
(OFDTS) is performed using temperature of the integrated reference (calibration) loop …
Epileptic seizure detection by exploiting temporal correlation of electroencephalogram signals
Electroencephalogram (EEG) has a great potential for diagnosis and treatment of brain
disorders like epileptic seizure. Feature extraction and classification of EEG signals is the …
disorders like epileptic seizure. Feature extraction and classification of EEG signals is the …
Features based on intrinsic mode functions for classification of EMG signals
In this paper, the features based on Intrinsic Mode Functions (IMFs) for classification of EMG
signals are presented. The EMD method decomposes EMG signals into a set of narrow …
signals are presented. The EMD method decomposes EMG signals into a set of narrow …